Using user input to adapt search results provided for presentation to the user

ABSTRACT

Methods, apparatus, and computer readable media related to interaction between a user and an automated assistant during a dialog between the user and the automated assistant. Some implementations are directed to adapting a graphical and/or audible presentation of search results provided by the automated assistant for presentation to the user. The adaptation may be in response to attribute(s), of one or more of the search results, referenced in spoken and/or typed textual input provided by the user during the dialog. Some of those implementations may enable a user to provide textual input to navigate the search results within the dialog and within resource and/or interface constraints associated with the dialog. Some of those implementations may additionally and/or alternatively enable adapting, based on textual input provided by a user to the automated assistant, when and/or whether search results having certain attributes are provided to the user by the automated assistant.

BACKGROUND

Automated assistants (also known as “personal assistant modules”,“mobile assistants”, or “chat bots”) may be interacted with by a uservia a variety of computing devices, such as smart phones, tabletcomputers, wearable devices, automobile systems, standalone personalassistant devices, and so forth. The automated assistants receivetextual input from the user (e.g., typed and/or spoken) and respond withresponsive output (e.g., visual and/or audible).

Some user interactions with an automated assistant may solicit only asingle response from the automated assistant. For example, textualinputs of “what time is it in London”, “what is the Capital ofDelaware”, and “how many ounces are in a cup” may all solicit a singleresponse from the automated assistant.

In contrast, other user interactions with an automated assistant may bemore general and solicit that a group of responses be provided by theautomated assistant. For example, “news headlines”, “nearbyrestaurants”, and “search results for mustang” may all solicit theautomated assistant to issue a search of one or more corpora and returna group of search results that are responsive to the search.

However, resource and/or interface constraints associated with existingautomated assistants may present one or more drawbacks in providing, bythe automated assistant, a group of search results for presentation to auser. For example, some automated assistants may be implemented via a“chat” type graphical user interface. Simultaneous display of a largequantity of search results in such an interface may clutter theinterface, make dialog harder to follow, and/or may consume a relativelylarge amount of computational resources. For instance, computationalresources may be consumed as a result of simultaneously rendering thelarge quantity of results and/or as a result of “scrolling” and/or otheractions that may be required to view the large quantity of results.Moreover, making the search results viewable in an interface and/orapplication that is separate from the automated assistant interfaceand/or application may consume greater user and/or computationalresources. For instance, switching to the other interface and/orapplication may distract the user from ongoing dialog with the automatedassistant and/or require that a computing device execute the separateapplication and/or render the separate interface.

As another example, some automated assistants may include (or berestricted to) providing audible user interface output and acceptingspoken user interface input. When a group of search results are providedby such automated assistants, directed navigation of the group ofresults by the user via spoken user interface input may not be possibleand/or may be limited. Moreover, many automated assistants may lack theability to adapt, based on textual input provided by a user to theautomated assistant, when and/or whether search results having certainattributes are provided to the user by the automated assistant.Additional and/or alternative drawbacks of these and/or other techniquesmay be presented.

SUMMARY

This specification is directed to methods, apparatus, and computerreadable media related to interaction between a user and an automatedassistant during a dialog between at least the user and the automatedassistant. Some implementations are directed to adapting a graphicaland/or audible presentation of search results provided by the automatedassistant for presentation to a user. The search result may be adaptedin response to attribute(s), of one or more of the search results,referenced in spoken and/or typed textual input provided by the userduring the dialog. Some of those implementations may enable a user toprovide textual input to navigate the search results within the dialogand within resource and/or interface constraints associated with thedialog. Some of those implementations may additionally or alternativelyenable adapting, based on textual input provided by a user to theautomated assistant, when and/or whether search results having certainattributes are provided to the user by the automated assistant.

Some of these and other implementations of the specification may achievevarious technical advantages. For example, some implementations ofadapting when and/or whether search results are provided to the user mayenable fewer search results to be provided to the user in somesituations, while still satisfying the informational needs of the user.This may reduce the use of various computational resources, such asresources of a computing device that are required for visually and/oraudibly presenting the search results to the user. Also, for example,some implementations of navigating the search results within the dialogmay enable a user to freely navigate (optionally non-sequentially)forward and/or backward through search results, without necessitatingthat all of the search results be presented simultaneously and/or thatthe entirety of one or more of the search results be presented. This mayenable search results to be provided for presentation to a user, andnavigated by the user, while also enabling: desired and/or necessaryresource and/or interface constraints to be satisfied; and/or less thanthe entirety of one or more of the search results to be provided duringthe presentation of the search results.

As one example, a user may cause textual input to be provided to anautomated assistant during a dialog between the user and the automatedassistant. The textual input may initiate the dialog or may be acontinuation of a previously initiated dialog. The textual input may benatural language free-form input, such as textual input that is based onuser interface input generated by the user via one or more userinterface input devices (e.g., based on typed input provided via aphysical or virtual keyboard or based on spoken input provided via amicrophone). As used herein, free-form input is input that is formulatedby a user and that is not constrained to a group of options presentedfor selection by the user (e.g., not constrained to a group of optionspresented in a drop-down menu).

In response to some textual input provided to the automated assistant aspart of a dialog, the automated assistant may obtain a plurality ofresponsive search results. For example, the automated assistant maycause a search of one or more databases to be issued based on thetextual input (or may itself search the database(s)), and may obtain aplurality of search results in response to the search. For example, inresponse to textual input of “news headlines”, a database of news storydocuments may be searched, and a plurality of search results obtainedthat are each based on a respective one of a plurality of recent newsstory documents identified in response to the search. The obtainedsearch results may be selected and/or ranked based on various signals,such as popularity of the search results, a degree of matching betweenthe search parameters and the search results, attributes of the user,etc.

Also, in some implementations the obtained search results mayadditionally or alternatively be selected and/or ranked based on pasttextual input provided by the user during dialog with the automatedassistant. For example, search results from “Source 1” (e.g., aparticular resource name of a URL or other indicator of a publisherand/or author) may be excluded from search results if: a user waspreviously presented with a search result from Source 1 during a dialogwith the automated assistant, and the user provided responsive textualinput of “no more from this source.” As another example, search resultsfrom “Source 2” may be promoted in the ranking of search results if: auser was previously presented with a search result from Source 2 duringa dialog with the automated assistant, and the user provided responsivetextual input of “I like this source.”

Regardless of the technique(s) for obtaining and/or ranking the searchresults, the automated assistant may sequentially provide groups of thesearch results for presentation (visual and/or audible) to the user aspart of the dialog. The order in which the search results are providedfor presentation may be based on the ranking. For example, a first groupthat includes only a highest ranked search result may be provided forpresentation, then a second group that includes only the second highestranked search result may be provided for presentation, etc. In someimplementations, the second group is automatically provided followingthe first group (e.g., immediately following or after a time delay). Insome implementations, a user interface input may be required before thesecond group is provided. For example, the second group may only beprovided if the user provides particular spoken user interface inputsuch as “next”, “another”, “continue”, etc. during or after theproviding of the first group.

In some implementations, during the providing of the search results theuser may provide further textual input that “breaks” the order of thesequential providing and causes one or more “out of order” searchresults to instead be provided. For example, the user may speak or type:“previous” to go back to the immediately preceding search result;“second result” to go back to the search result presented second; “backto the one from Source A” to go back to the search result from “SourceA”; “back to the one about Topic A” to go back to the search resultabout “Topic A”; “more from Source A” to move forward to another searchresult from “Source A” (even though it's not next in the sequentialorder); “more about Topic A” to move forward to another search resultabout “Topic A” (even though it's not next in the sequential order);and/or “more like this” to move forward to an additional search resultthat is similar to the search result that has most recently beenpresented at least in part (even though the additional search result isnot next in the sequential order). These and other implementations aredescribed in additional detail herein.

In some implementations, a method performed by one or more processors isprovided that includes receiving input and obtaining a plurality ofsearch results that are responsive to the input. The input is based onuser interface input generated by a user via a user interface inputdevice, and the user interface input is generated by the user as part ofdialog between the user and an automated assistant implemented at leastin part by one or more of the processors. The method further includessequentially providing groups of the search results for presentation tothe user via a user interface output device. Each of the groups of thesearch results includes at least one of the search results, andsequentially providing the groups of search results includes providingeach of the groups according to an order of the groups. The methodfurther includes receiving further input during the providing. Thefurther input is based on further user interface input generated by theuser via the user interface input device or another user interface inputdevice. The method further includes determining, based on one or moreterms of the further input and based on at least one attribute of apreviously presented search result, that the further input correspondsto the previously presented search result. The previously presentedsearch result is one of the search results that was previously providedduring the sequentially providing. The method further includes, inresponse to determining that the further input corresponds to thepreviously presented search result, providing output related to thepreviously presented search result. The providing of the output iscounter to the order of the groups.

These and other implementations of technology disclosed herein mayoptionally include one or more of the following features.

In some implementations, sequentially providing the groups of the searchresults for presentation to the user includes providing an initial groupof the groups in response to the input and providing a second groupfollowing the initial group. The second group sequentially follows theinitial group according to the order, providing the second group is inresponse to receiving additional user interface input, and theadditional user interface input precedes the further user interfaceinput. In some of those implementations, the additional user interfaceinput is verbal user interface input.

In some implementations, the user interface input device is amicrophone, the user interface output device is a speaker, and thefurther user interface input is generated by the user via themicrophone. In some of those implementations, at least part of thefurther user interface input is received during audible presentation ofone of the search results via the speaker.

In some implementations, the further user interface input is verbal userinterface input, and the method further includes actively monitoring forthe further input during the providing. In some of thoseimplementations, receiving the further input occurs during the activelymonitoring.

In some implementations, the user interface output device is a display,the output includes the previously presented search result, andproviding the output includes: causing the output to be presented, in agraphical user interface presented on the display, separate and apartfrom any persistent output from the previous presentation of thepreviously presented search result.

In some implementations, the output is an additional search result thatis related to the previously presented search result, but that isassociated with a different underlying content item than the previouslypresented search result.

In some implementations, the user interface output device is a displayand sequentially providing the groups for presentation to the user viathe display includes: causing each of the groups to supplant, in agraphical user interface presented on the display, a correspondingimmediately preceding group of the groups. In some of thoseimplementations, providing the output related to the search resultincludes causing the output to supplant, in the graphical userinterface, a most recently provided group of the groups provided duringthe sequentially providing.

In some implementations, determining, based on the one or more terms ofthe further input and based the attribute of the previously presentedsearch result, that the further input corresponds to the previouslypresented search result includes: identifying the attribute of thepreviously presented search result, and determining that the one or moreterms match the attribute. In some of those implementations, theattribute is one of: a name of a source of the previously presentedsearch result, a name of an entity included in the previously presentedsearch result, and a reference to a presentation order of the previouslypresented search result in the sequentially providing of the groups.

In some implementations, the method further includes determining, basedon one or more of the terms of the further input, that the further inputalso corresponds to an additional previously presented search result, ofthe search results, that was previously provided during the sequentiallyproviding. In some of those implementations, determining that thefurther input corresponds to the previously presented search resultfurther includes selecting the previously presented search resultinstead of the additional previously presented search result based onone or more additional criteria. In some of those implementations,determining that the further input corresponds to the previouslypresented search result includes: generating a prompt based on anadditional attribute of the additional previously presented searchresult; providing the prompt for presentation to the user via the userinterface output device; receiving, responsive to the prompt, additionaluser interface input; and selecting the previously presented searchresult instead of the additional previously presented search resultbased on the additional user interface input.

In some implementations, the input and/or the further textual inputcomprise textual input, such as textual input generated based on verbaluser interface input.

In some implementations, a method performed by one or more processors isprovided that includes, as part of a dialog between a user and anautomated assistant implemented at least in part by one or more of theprocessors: providing a search result for presentation to the user; inresponse to providing the search result, receiving textual input thatreferences an attribute of the search result and a sentiment of the userfor that attribute; determining, based on the attribute of the searchresult and the sentiment of the user for that attribute, a parameterthat influences whether or when one or more attribute search resultsthat have the attribute are provided by the automated assistant inresponse to further dialog between the user and the automated assistant;and as part of the further dialog between the user and the automatedassistant: using, by the automated assistant, the parameter to influencewhether or when at least one of the attribute search results is providedfor presentation to the user.

These and other implementations of technology disclosed herein mayoptionally include one or more of the following features. In someimplementations, the parameter either: influences when the attributesearch results are provided as part of the further dialog by influencinga ranking of the attribute search results, or influences whether theattribute search results are provided as part of the further dialog bypreventing the attribute search results from being provided. In someversions of those implementations, the parameter influences the rankingof the attribute search results to a degree that is based on thesentiment of the user for the attribute of the attribute search results.In some other versions of those implementations, the parameter preventsthe attribute search results from being provided based on the sentimentof the user for the attribute being expressed in the textual input byone or more predefined terms.

In some implementations, the attribute of the search result is a sourceof the search result, the textual input includes a term that referencesthe source and an additional term that references the sentiment, and theparameter influences a ranking of the attribute search results from thesource, or prevents the attribute search results from the source frombeing provided for presentation to the user.

In some implementations, the reference to the attribute in the textualinput is a reference to the search result without an explicit referenceto the attribute. In some of those implementations, the method furtherincludes identifying the attribute based on the attribute being assignedto the search result in one or more computer readable media. In someversions of those implementations, the attribute is one of: a source ofthe search result, an entity referenced in the search result, and adocument type of the search result.

In some implementations, the search result is provided for presentationto the user in response to first textual input of the user, the furtherdialog is a continuance of the dialog and is a continuance of sequentialpresentation of search results responsive to the first textual input,and the at least one of the attribute search results is responsive tothe first textual input.

In some implementations, the search result is provided for presentationto the user in response to first textual input of the user, the furtherdialog includes providing additional search results to the user inresponse to a new search issued for additional textual input of theuser, and the at least one of the attribute search results is responsiveto the additional textual input.

In addition, some implementations include one or more processors of oneor more computing devices, where the one or more processors are operableto execute instructions stored in associated memory, and where theinstructions are configured to cause performance of any of theaforementioned methods. Some implementations also include one or morenon-transitory computer readable storage media storing computerinstructions executable by one or more processors to perform any of theaforementioned methods.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts described in greater detail herein arecontemplated as being part of the subject matter disclosed herein. Forexample, all combinations of claimed subject matter appearing at the endof this disclosure are contemplated as being part of the subject matterdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in whichimplementations disclosed herein may be implemented.

FIG. 2 illustrates an example of adapting search results provided to auser by an automated assistant during a dialog, where the search resultsare adapted in response to attribute(s), of one or more of the searchresults, referenced in spoken and/or typed textual input provided by theuser during the dialog.

FIG. 3 illustrates an example client computing device with a displayscreen displaying an example of dialog that may occur between a user ofthe client computing device and an automated assistant according toimplementations disclosed herein.

FIG. 4A, FIG. 4B, FIG. 4C, and FIG. 4D illustrate the example clientcomputing device of FIG. 3 , with part of a display screen displayinganother example of dialog that may occur between a user of the clientcomputing device and an automated assistant according to implementationsdisclosed herein.

FIG. 4E illustrates the example client computing device of FIG. 3 , witha display screen displaying yet another example of dialog that may occurbetween a user of the client computing device and an automated assistantaccording to implementations disclosed herein.

FIG. 5 illustrates another example client computing device, and anexample of audible dialog that may occur between a user of the clientcomputing device and an automated assistant according to implementationsdisclosed herein.

FIG. 6 and FIG. 7 illustrate the example client computing device of FIG.3 , with a display screen displaying yet further examples of dialog thatmay occur between a user of the client computing device and an automatedassistant according to implementations disclosed herein.

FIG. 8 is a flowchart illustrating an example method according toimplementations disclosed herein.

FIG. 9 is a flowchart illustrating another example method according toimplementations disclosed herein

FIG. 10 illustrates an example architecture of a computing device.

DETAILED DESCRIPTION

In FIG. 1 , an example environment in which techniques disclosed hereinmay be implemented is illustrated. The example environment includes oneor more user interface input devices 102, one or more user interfaceoutput devices 104, and an automated assistant 120. The user interfaceinput devices 102 may include, for example, a physical keyboard, a touchscreen (e.g., implementing a virtual keyboard or other textual inputmechanism), and/or a microphone. The user interface output devices 104may include, for example, a display screen, and/or speaker(s). The userinterface input and output devices 102, 104 may be incorporated on oneor more computing devices of a user. For example, a mobile phone of theuser may include the user interface input and output devices 102, 104;or a standalone personal assistant hardware device may include the userinterface input and output devices 102, 104; or a first computing devicemay include the user interface input device(s) 102 and a separatecomputing device may include the user interface output device(s) 104;etc.

Although automated assistant 120 is illustrated in FIG. 1 as separatefrom the user interface output and input devices 102, 104, in someimplementations all or aspects of the automated assistant 120 may beimplemented on a computing device that also contains the user interfaceinput device(s) 102 and/or the user interface output device(s) 104. Forexample, all or aspects of output generation engine 130 and/or outputadaptation engine 124 of automated assistant 120 may be implemented onthe computing device. In some implementations, all or aspects of theautomated assistant 120 may be implemented on computing device(s) thatare separate and remote from a computing device that contains the userinterface input devices 102 and/or the user interface output devices 104(e.g., all or aspects may be implemented “in the cloud”). In some ofthose implementations, those aspects of automated assistant 120 maycommunicate with the computing device via one or more networks such as alocal area network (LAN) and/or wide area network (WAN) (e.g., theInternet).

Some non-limiting examples of client computing device(s) that mayinclude the user interface input device(s) 102 and/or user interfaceoutput device(s) 104 include one or more of: a desktop computing device,a laptop computing device, a standalone hardware device at least in partdedicated to automated assistance, a tablet computing device, a mobilephone computing device, a computing device of a vehicle of the user(e.g., an in-vehicle communications system, an in-vehicle entertainmentsystem, an in-vehicle navigation system), or a wearable apparatus of theuser that includes a computing device (e.g., a watch of the user havinga computing device, glasses of the user having a computing device, avirtual or augmented reality computing device). Additional and/oralternative client computing devices may be provided. In someimplementations, a given user may communicate with all or aspects ofautomated assistant 120 utilizing a plurality of client computingdevices that collectively form a coordinated “ecosystem” of computingdevices. However, for the sake of brevity, some examples described inthis disclosure will focus on the user operating a single clientcomputing device.

A client computing device and automated assistant 120 may each (whenthey are separate devices) include one or more memories for storage ofdata and software applications, one or more processors for accessingdata and executing applications, and other components that facilitatecommunication over a network. The operations performed by one or more ofthe client computing devices and/or by the automated assistant 120 maybe distributed across multiple computing devices. Automated assistant120 may be implemented as, for example, computer programs running on oneor more computers in one or more locations that are coupled to eachother through a network.

As illustrated in FIG. 1 , a user provides input to the automatedassistant 120 via the user interface input device(s) 102. The automatedassistant 120 provides responsive output for presentation to the uservia the user interface output devices(s) 104 (optionally after furtherprocessing by one or more components). For the sake of simplicity, theinput is illustrated in FIG. 1 as being provided directly to theautomated assistant 120 by the user interface input device(s) 102 andthe output is illustrated as being provided by the automated assistant120 directly to the user interface output device(s) 104. However, it isnoted that in various implementations one or more intermediary hardwarecomponents may be functionally interposed between the automatedassistant 120 and the user interface input and/or output devices 102,104, and may optionally process the input and/or output. For example,one or more components may process the output provided by automatedassistant 120 and generate, based on the processing of the output, oneor more signals for presentation of search results and/or other contentvia the user interface output device(s) 104. For instance, where a userinterface output device 104 is on a client computing device separatefrom all or aspects of the automated assistant 120, a hardware processorand/or other components may process the output and generate, based onthe output, signals for driving the user interface output device 104.

In some implementations, the input received by the automated assistant120 is already in a textual format. For example, the user interfaceinput device(s) 102 may include a keyboard that generates textual inputin response to user interface input directed to the keyboard, and thetextual input provided to the automated assistant 120. Also, forexample, the user interface input device(s) 102 may include amicrophone, a voice-to-text processor that is separate from theautomated assistant 120 may convert voice input received at themicrophone into textual input, and the textual input may be provided tothe automated assistant 120. In some other implementations, the inputinitially received by the automated assistant 120 is not in a textualformat, but is converted to a textual format by the automated assistant120 and provided in the textual format to one or more other componentsof the automated assistant 120. For instance, the user interface inputdevice(s) 102 may include a microphone, voice input may be received atthe microphone and provided to the automated assistant 120, and avoice-to-text processor of the automated assistant 120 may convert thevoice input into textual input.

A client computing device may optionally operate one or moreapplications that enable dialog with the automated assistant 120. Suchapplications may come in various forms such as a short messaging service(“SMS”) and/or multimedia messaging service (“MMS”) client, an onlinechat client (e.g., instant messenger, Internet relay chat, or “IRC,”etc.), a messaging application associated with a social network, anautomated assistant messaging service dedicated to conversations withautomated assistant 120, and so forth. In some implementations, one ormore of the applications may be implemented via a webpage or otherresources rendered by a web browser or other application of a clientcomputing device.

In various implementations, in response to certain textual inputprovided to the automated assistant 120 during a dialog with theautomated assistant 120, the automated assistant 120 may obtain aplurality of responsive search results using search parameters that arebased at least in part on the textual input. The obtained search resultsmay be selected and/or ranked based on various signals, such aspopularity of the search results, a degree of matching between thesearch parameters and the search results, attributes of the user, etc.In some implementations, the obtained search results may additionally oralternatively be selected and/or ranked based on past textual inputprovided by the user during dialog with the automated assistant. Forexample, search results from “Source 1” may be excluded from searchresults to be provided for presentation to a user if: a user waspreviously presented with a search result from Source 1 during dialogwith the automated assistant 120, and the user provided responsivetextual input of “no more from this source.”

The automated assistant 120 may then sequentially provide, as output forproviding to the user via user interface output device(s) 104, groups ofsearch results for presentation (visual and/or audible) to the user aspart of the dialog. The automated assistant 120 may sequentially providethe groups of search results in an order that is based on the ranking ofthe search results. In some implementations, each of the provided groupsmay include only a single search result. In some other implementations,one or more of the provided groups may include multiple search results(e.g., each group may include two search results).

In some implementations, during the providing of the search results bythe automated assistant 120, the user may provide further textual inputthat “breaks” the order of the sequential providing by the automatedassistant 120 and causes one or more “out of order” search results toinstead be provided by the automated assistant 120. For example, theuser may speak or type: “previous” to go back to the immediatelypreceding search result; “second result” to go back to the search resultpresented second; “back to the one from Source A” to go back to thesearch result from “Source A”; “back to the one about Topic A” to goback to the search result about “Topic A”; “more from Source A” to moveforward to another search result from “Source A” (even though it's notnext in the sequential order), and/or “more about Topic A” to moveforward to another search result about “Topic A” (even though it's notnext in the sequential order).

In various implementations, automated assistant 120 may include amessage processing engine 122, an output adaptation engine 124, a searchengine 126, a ranking engine 128, and an output generation engine 130.In some implementations, one or more of engines 122, 124, 126, 128,and/or 130 may be omitted, combined, and/or implemented in a componentthat is separate from automated assistant 120. For example, one or moreof engines 122, 124, 126, 128, and/or 130, or any operative portionthereof, may be implemented in a component that is executed by a clientcomputing device that includes the user interface input and/or outputdevices 102 and 104 and that is separate from the automated assistant120. Also, for example, the search engine 126 and/or the ranking engine128 may be implemented in whole or in part by a system that is separatefrom the automated assistant 120 (e.g., a separate search system incommunication with the automated assistant 120).

Message processing engine 122 processes textual input that is submittedto the automated assistant 120 and generates annotated output for use byone or more other components of the automated assistant 120. Forexample, the message processing engine 122 may process natural languagefree-form textual input that is generated based on user interface inputgenerated by a user via user interface input device(s) 102. Thegenerated annotated output includes one or more annotations of thetextual input and optionally one or more (e.g., all) of the terms of thetextual input.

In some implementations, the message processing engine 122 is configuredto identify and annotate various types of grammatical information intextual input. For example, the message processing engine 122 mayinclude a part of speech tagger configured to annotate terms with theirgrammatical roles. For example, the part of speech tagger may tag eachterm with its part of speech such as “noun,” “verb,” “adjective,”“pronoun,” etc. Also, for example, in some implementations the messageprocessing engine 122 may additionally and/or alternatively include adependency parser configured to determine syntactic relationshipsbetween terms in textual input. For example, the dependency parser maydetermine which terms modify other terms, subjects and verbs ofsentences, and so forth (e.g., a parse tree)—and may make annotations ofsuch dependencies.

In some implementations, the message processing engine 122 mayadditionally and/or alternatively include an entity tagger configured toannotate entity references in one or more segments such as references topeople, organizations, locations, and so forth. The entity tagger mayannotate references to an entity at a high level of granularity (e.g.,to enable identification of all references to an entity class such aspeople) and/or a lower level of granularity (e.g., to enableidentification of all references to a particular entity such as aparticular person). The entity tagger may rely on content of the naturallanguage input to resolve a particular entity and/or may optionallycommunicate with a knowledge graph or other entity database to resolve aparticular entity.

In some implementations, the message processing engine 122 mayadditionally and/or alternatively include a coreference resolverconfigured to group, or “cluster,” references to the same entity basedon one or more contextual cues. For example, the coreference resolvermay be utilized to resolve the term “it” to “Source 1” in the naturallanguage input “I hate Source 1. No more results from it.”

In some implementations, the message processing engine 122 mayadditionally and/or alternatively determine a sentiment, and optionallya sentiment magnitude, of one or more segments of textual input. Thesentiment of a segment may be determined based on term(s) of thesegment, other term(s) of the textual input, and/or data that is inaddition to the textual input itself (e.g., voice characteristicsincluded in voice input on which the textual input is based, precedingtextual input, and/or other data). In some implementations, the messageprocessing engine 122 includes a trained sentiment classifier trained topredict, based on a segment of textual input and/or other data, aclass/direction of sentiment of the segment and optionally a magnitudeof the sentiment. For example, the sentiment classifier may predictwhether a segment is positive or negative, and optionally a magnitude ofthe positivity/negativity, based on term(s) of the segment andoptionally based on other data. In some implementations, the messageprocessing engine 122 additionally and/or alternatively utilizes amapping between terms and sentiments (and optionally sentimentmagnitudes) to determine sentiment of a segment. For example, themapping may define that: a segment that includes “never” has negativesentiment of a strong magnitude; “I'm not a fan of” has negativesentiment of a lesser magnitude; “always” has positive sentiment of astrong magnitude; “I like” has positive sentiment of a lesser magnitude;etc.

In some implementations, one or more components of the messageprocessing engine 122 may rely on annotations from one or more othercomponents of the message processing engine 122. For example, in someimplementations the named entity tagger may rely on annotations from thecoreference resolver and/or dependency parser in annotating all mentionsto a particular entity. Also, for example, in some implementations thecoreference resolver may rely on annotations from the dependency parserin clustering references to the same entity. In some implementations, inprocessing particular textual input, one or more components of themessage processing engine 122 may use related prior input and/or otherrelated data outside of the particular textual input to determine one ormore annotations. For example, a first textual input of a user in adialog with the automated assistant 120 may be “Is that result fromsource 1?” and a subsequent textual input of the user may be “neverprovide results from them”. In processing “never provide results fromthem”, the coreference resolver may resolve “them” to “source 1”utilizing the prior input of “Is that result from source 1”.

The search engine 126 searches one or more search databases 154 inresponse to at least some textual input submitted by a user as part ofthe dialog between the automated assistant 120 and the user. The searchengine 126 searches the search databases 154 to identify content that isresponsive to the textual input. In some implementations, the searchdatabases 154 include database(s) that index publicly available contentand/or database(s) that index content that is private to the user. Thesearch engine 126 may utilize the databases 154 to identify responsivecontent and may generate search results based on the identifiedresponsive content. In some implementations, one or more of the searchdatabases 154 may be remote from the automated assistant 120 and/or anyseparate client computing device, and/or one or more of the searchdatabases 154 may be local to the automated assistant 120 and/or anyseparate client computing device. In this specification, the term“database” is used to refer to any collection of structured orunstructured data stored in one or more computer readable media.

The search engine 126 may utilize various techniques in searching thesearch databases 154 in response to textual input, such as conventionaland/or other information retrieval techniques. In some implementations,the search engine 126 may search one or more of the databases 154 basedon search parameter(s) that conform strictly to the textual input. Forexample, for textual input of “mustangs”, the only search parameter maybe the term “mustangs”. In some implementations, the search engine 126may search one or more of the databases 154 based on one or more searchparameters that are based on, but that do not necessarily conformstrictly to, the textual input. For example, for textual input of “localnews”, the search engine 126 may search one or more of the databases 154based on a search parameter that restricts the databases 154 to “news”databases and/or content to “news” content, and based on a searchparameter that restricts content to content that is local to a user. Asanother example, for textual input of “restaurants nearby”, the searchengine 126 may search one or more of the databases 154 based on a searchparameter that restricts the databases 154 to “points of interests”databases and/or content to “restaurant” content, and based on a searchparameter that restricts content to content that is within a thresholddistance of a current location of the user. As yet another example, fortextual input of “my photos”, the search engine 126 may search one ormore of the databases 154 based on a search parameter that restricts thedatabases 154 to databases that are personal to the user and/or contentto “image” content.

The ranking engine 128 calculates scores for the content identified bysearch engine 126 using one or more ranking signals, such as popularityof the content, a degree of matching between the search parameters andthe content, attributes of the user (e.g., a location of the user, aprimary language of the user), etc. The ranking engine 128 then ranksthe responsive content using the scores.

The search engine 126 uses the identified responsive content ranked bythe ranking engine 128 to generate search results. The search resultsinclude search results corresponding to the content that is responsiveto the search issued based on the textual input. For example, each ofthe search results can include a title or other synopsis of a responsivecontent item, a summary of the content item, a link to the responsivecontent item, other information related to the responsive content item,and/or even the entirety of the content item. As one example, thesummary of a news story content item may include a particular “snippet”or section of the news story. Also, for example, for a search resultassociated with an image, the search result may include a reduced sizedisplay of the image, a title associated with the image, and/or a linkto the image. Also, for example, for a search result associated with avideo, the search result may include an image from the video, a segmentof the video, a title of the video, and/or a link to the video.

As described herein, in some implementations content may be identifiedand/or ranked based on past textual input provided by the user duringdialog with the automated assistant 120. For example, the search engine126 may search the search databases 154 based on parameter(s) determinedbased on past textual input and/or the ranking engine 128 may rankcontent based on parameter(s) determined based on past textual input.For example, in ranking content identified by the search engine 126,ranking engine 128 may filter out any content items about “Topic A” if auser was previously presented with a search result about Topic A, andthe user provided responsive textual input of “never provide resultsabout this topic.” As another example, in ranking content, rankingengine 128 may promote content items from “Source 2” if a user waspreviously presented with a search result from Source 2, and the userprovided responsive textual input of “I like this source.”

The ranked search results generated by the search engine 126 and theranking engine 128 are provided by the output generation engine 130 forpresentation to the user via the user interface output device(s) 104.For example, the output generation engine 130 may provide the searchresults for audible and/or visual presentation via the user interfaceoutput device(s) 104 and may provide the search results as part of thedialog between the user and the automated assistant 120.

In some implementations, the output generation engine 130 sequentiallyprovides groups of search results for presentation to the user as partof the dialog, and the order in which the search results are providedmay be based on the ranking of the search results. For example, theoutput generation engine 130 may provide a first group that includesonly a highest ranked search result, then a second group that includesonly the second highest ranked search result, then a third group thatincludes the third highest ranked search result, etc. In someimplementations, the output generation engine 130 automatically providesthe second group of search results following the first group (e.g.,immediately following or after a time delay), automatically provides thethird group following the second group, etc. In some implementations,the output generation engine 130 awaits a further user interface inputbefore providing the second group, then awaits a further user interfaceinput before providing the third group, etc. For example, in some ofthose implementations the output generation engine 130 may only providethe second group if the user provides spoken user interface input suchas “next”, “another”, etc. during or after the providing of the firstgroup. Other user interface inputs may be utilized to cause the outputgeneration engine 130 to sequentially progress through groups of searchresults.

The output adaptation engine 124 adapts the providing of the searchresults by the output generation engine 130 in response to certainfurther textual input of the user. In some implementations, the outputadaptation engine 124 may break the order of the sequential providing ofthe search results by the output generation engine 130 in response tocertain further textual input. For example, the output adaptation engine124 may determine that at least one term of the further textual inputincludes an attribute of one or more search results that are notincluded in the next group to be provided based on the order. Based atleast in part on the determination, the output adaptation engine 124 mayprovide one or more of the search results having the attribute, andprovide the search result(s) in lieu of the next group to be providedbased on the order.

To illustrate some examples, assume the output generation engine 130provided Search Result A, then provided Search Result B, is providingSearch Result C, and is slated to provide Search Result D followingSearch Result C. In response to textual input of “back to the firstresult” provided prior to the providing of Search Result D, the outputadaptation engine 124 may cause the output generation engine 130 toagain provide Search Result A in lieu of Search Result D. This may bebased on determining that the term “back” references a previouslypresented search result and based on determining that the term “firstresult” matches an attribute of previously presented Search Result A(the attribute of being the search result provided first by outputgeneration engine 130). In response to textual input of “back to thesearch result about Topic A” provided prior to the providing of SearchResult D, the output adaptation engine 124 may cause the outputgeneration engine 130 to again provide Search Result B in lieu of SearchResult D. This may be based on determining that the term “back”references a previously presented search result and based on determiningthat previously presented Search Result B is related to “Topic A” (andthat Search Results A and C are not related to Topic A and/or are lessstrongly related to Topic A). Determining that a search result isrelated to a topic, related to an entity, is from a particular source,and/or has other attribute(s) may be based on those attributes beingassigned to the search result and/or underlying content in searchdatabase(s) 154 and/or in other database(s).

In response to textual input of “more results like this one” providedprior to the providing of Search Result D, the output adaptation engine124 may cause the output generation engine 130 to provide a yet to bepresented Search Result X in lieu of Search Result D. This may be basedon determining that “more” has a positive sentiment (e.g., based on anannotation of message processing engine 122), that “like this one”references attribute(s) of the previously presented (at least in part)Search Result C, and that one or more attributes of Search Result C aremore similar to attributes of Search Result X than they are toattributes of Search Result D. In response to textual input of “I don'tlike this source” provided prior to the providing of Search Result D,the output adaptation engine 124 may cause the output generation engine130 to provide a yet to be presented Search Result Y in lieu of SearchResult D. This may be based on determining that “don't like” has anegative sentiment (e.g., based on an annotation of message processingengine 122), that “this source” references a source attribute of thepreviously presented (at least in part) Search Result C, and that SearchResult D has the same source attribute as Search Result C, whereas anext in the order Search Result Y does not have the same sourceattribute as Search Result C. In implementations of the various examplesprovided, the output adaptation engine 124 may rely on one or moreannotations provided by message processing engine 122 and may utilizevarious techniques for semantic understanding of textual segments suchas rules-based techniques, template-based techniques, machine learningmodels (e.g., deep neural networks), and/or other techniques.

In some implementations, the output adaptation engine 124 additionallyand/or alternatively utilizes expressed sentiments for attributes tomodify the identification and/or ranking of search results fordownstream searches based on downstream textual input in dialog betweenthe user and the automated assistant 120. For example, the textual inputabove of “I don't like this source” may be used to demote the ranking ofdownstream search results that have the same source attribute as SearchResult C.

Turning now to FIG. 2 , additional description is provided of variouscomponents of automated assistant 120. In FIG. 2 , the messageprocessing engine 122 receives textual input 201, generates annotatedinput 203 that includes annotations of the textual input and/or terms oftextual input 201 itself. The search engine 126 utilizes the annotatedinput 203 to determine search parameters, issues a search of one or moreof the search databases 154 based on the search parameters, andidentifies content that is responsive to the search. The ranking engine128 ranks the responsive content utilizing one or more signals. Forexample, the ranking engine 128 may rank responsive content based onpopularity of the responsive content (e.g., as indicated by entries forthe content in search database(s) 154) and/or based on one or moreparameters of parameters database 156. As described herein, one or moreparameters of parameters database 156 may be determined based on pasttextual input provided in a dialog with a user. A given parameter maydefine an attribute and how to influence search results having thatattribute. For instance, the parameter may define an attribute anddefine how search results having that attribute should be promoted ordemoted in ranking of search results, or that search results having thatattribute should be prevented from being included in search resultsprovided for presentation to a user.

The ranking engine 128 provides search results and an order of thesearch results 205 to the output generation engine 122. The searchresults are based on the content identified by search engine 126 and theorder of the search results may be based on the ranking determined bythe ranking engine 128.

The output generation engine 130 begins sequentially providing thesearch results based on the order. For example, as illustrated in FIG. 2, the output generation engine 130 provides search result group 1 207and optionally further search result groups as indicated by theellipsis.

At some point during the sequential providing of the search results byoutput generation engine 130, the message processing engine 122 receivesa further textual input 211. The message processing engine 122 maydetermine the further textual input 211 relates to adaptation of thepresentation of the search results and, as a result, provide furtherannotated input 213 to output adaptation engine 124. The furtherannotated input 213 may include annotations of the further textual input211 and/or one or more terms of the further textual input 211.

In some implementations, based on the further annotated input 213, theoutput adaptation engine 124 determines an adaptation 215 to be made tothe sequential providing of the search results and communicates theadaptation 215 to the output generation engine 130. The outputgeneration engine 130 may adapt the current sequential providing of thesearch results based on the adaptation 215. For example, instead ofproviding a next search result in the current sequential providing, theoutput generation engine 130 may instead provide an out of order searchresult based on the adaptation 215.

In some implementations, based on the further annotated input 213, theoutput adaptation engine 124 additionally or alternatively determinesparameter(s) 217 for storing in parameters database 156. These storedparameter(s) 217 may be used to modify further downstream searchingand/or ranking by the engines 126 and/or 128 in response to new searchesbased on new textual input. It is noted that in some implementation theoutput adaptation engine 124 may both provide both the adaptation 215and the parameter(s) 217. For example, for further textual input 211 of“I never want results from Source A”: the adaptation 215 may be providedand cause a search result that is from Source A and that is slated to beprovided by the output generation engine 130 to no longer be provided;and the parameter 217 may be stored and may prevent further searchresults from Source A from being provided in response to new searchesthat are based on new textual input.

Referring now to FIGS. 3-7 , various examples of implementations of theautomated assistant 120 are described. FIGS. 3, 4A-4E, 6, and 7 eachillustrate a computing device 110 with a display screen 140 displayingexamples of dialog that may occur between a user of the computing device110 and the automated assistant 120 according to implementationsdisclosed herein. One or more aspects of the automated assistant 120 maybe implemented on the computing device 110 and/or on one or morecomputing devices that are in network communication with the computingdevice 110.

The display screen 140 of FIGS. 3, 4A-4E, 6, and 7 further includes atextual reply interface element 188 that the user may select to generateuser interface input via a virtual keyboard and a voice reply interfaceelement 189 that the user may select to generate user interface inputvia a microphone. In some implementations, the user may generate userinterface input via the microphone without selection of the voice replyinterface element 189. For example, during the dialog, active monitoringfor audible user interface input via the microphone may occur to obviatethe need for the user to select the voice reply interface element 189.In some of those and/or in other implementations, the voice replyinterface element 189 may be omitted. Moreover, in some implementations,the textual reply interface element 188 may additionally and/oralternatively be omitted (e.g., the user may only provide audible userinterface input). The display screen 140 of FIGS. 3, 4A-4E, 6, and 7also includes system interface elements 181, 182, 183 that may beinteracted with by the user to cause the computing device 110 to performone or more actions.

FIG. 5 illustrates a computing device 110 that includes one or moremicrophones and one or more speakers and illustrates examples of dialogthat may occur, via the microphone(s) and speaker(s), between a user 101of the computing device 510 and the automated assistant 120 according toimplementations described herein. One or more aspects of the automatedassistant 120 may be implemented on the computing device 510 and/or onone or more computing devices that are in network communication with thecomputing device 510.

In FIG. 3 , the user provides initial textual input 380A as part of adialog between the user and the automated assistant 120. In response tothe textual input 380A, the automated assistant 120 obtains searchresults that are responsive to the textual input 380A and provides asearch result 382A for presentation on the display screen 140 as part ofa transcript of the dialog. The automated assistant 120 provides thesearch result 382A for presentation first based on it being first in anorder of presentation of the search results (e.g., based on a ranking ofthe search results). The user then provides textual input 380B of “next”to cause the automated assistant 120 to provide the search result 382Bfor presentation. The search result 382B is the next search resultaccording to the order.

The user then provides textual input 380C of “More like this”. Theautomated assistant 120 determines that “like this” references the mostrecently presented search result 382B. The automated assistant 120further determines one or more attributes of the most recently presentedsearch result 382B, such as attributes of: the search result beingassociated with a first entity corresponding to the fictional chef JonDoe; and the search result being associated with a second entitycorresponding to the fictional restaurant Hypothetical Café. Theautomated assistant 120 further determines that, like search result382B, search result 382C also has an attribute of being associated withthe first entity corresponding to the fictional chef Jon Doe. Based onthat determination, the automated assistant 120 provides the searchresult 382C for presentation in response to the textual input 380C. Insome implementations, search result 382C may not be the sequentiallynext search result according to the original order. However, based onthe textual input 380C, the automated assistant 120 may provide thesearch result 382C in lieu of the next search result that was slated tobe provided according to the original order. In other words, the textualinput 380C may cause the automated assistant 120 to adapt the originalorder of presentation of the search results so that one or more searchresults that are similar to search result 382B are promoted in theorder.

The user then provides textual input 380D of “No, about HypotheticalCafé”. The automated assistant 120 determines, based on textual inputs380C and 380D, that “No, about Hypothetical Café” references that theuser wants more search results about the fictional restaurantHypothetical Café and not about the fictional chef Jon Doe. Theautomated assistant 120 further determines that search result 382D hasan attribute of being associated with the entity corresponding to thefictional restaurant Hypothetical Café, and provides the search result382D for presentation next based on that determination. In someimplementations, search result 382D may not be the next search resultaccording to the order as modified by textual input 380C. However, basedon the textual input 380D, the automated assistant 120 may providesearch result 382 instead of the next search result slated to beprovided according to the modified order. In other words, the textualinput 380D may cause the automated assistant 120 to further adapt themodified order of presentation of the search results so that one or moresearch results that relate to “Hypothetical Café” are promoted in theorder.

It is noted that in some implementations, in response to textual input380C, the automated assistant 120 may have identified multiple searchresults that share one or more attributes with search result 382B, andmay have selected the search result 382C instead of those other searchresults based on one or more factors. For example, the automatedassistant 120 may determine that the entity corresponding to thefictional chef Jon Doe has a greater weight for (e.g., is more stronglyassociated with) search result 382B than does the entity correspondingto the fictional restaurant Hypothetical Café. Search result 382C mayhave been initially selected in lieu of search result 382D and/or othersearch results based on search result 382C also being stronglyassociated with the entity corresponding to the fictional chef Jon Doe.

In some implementations, the automated assistant 120 may optionallygenerate a prompt in response to textual input 380C that solicits inputfor disambiguating “like this.” For example, the automated assistant 120may identify one or more attributes of the search result 382B andformulate a prompt that asks the user to specify which attribute(s) theuser means by “like this”. For instance, the automated assistant 120 maydetermine that the search result 382B is associated with a sourceattribute of “Source 2” and associated with entity attributes of “JonDoe (Chef)” and “Hypothetical Café”. Based on this determination, theprompt may be “do you want more from Source 2, more about the chef, ormore about Hypothetical Café?”. Further textual input provided inresponse to the prompt may be used by the automated assistant to selectappropriate further search result(s) to provide. For instance, furthertextual input of “about Hypothetical Café” may lead to search result382D initially being provided in lieu of search result 382C.

In FIG. 4A, the user provides initial textual input 480A as part of adialog between the user and the automated assistant 120. In response tothe textual input 480A, the automated assistant 120 obtains searchresults that are responsive to the textual input 480A and provides asearch result 482A for presentation on the display screen as part of thedialog. The automated assistant 120 provides the search result 482Afirst based on it being first in an order of presentation of the searchresults (e.g., based on a ranking of the search results).

After the search result 482A is displayed to the user, the user thenprovides textual input 488B of “next” as illustrated in FIG. 4B. Inresponse, the automated assistant 120 provides output that causes thesearch result 482A to be supplanted with the second search result 482Bas illustrated in FIG. 4B. The search result 482B is the next searchresult according to the order. The textual input 488B of FIG. 4B may beprovided by typing (e.g., by selecting interface area 184) or by voice(e.g., by selecting microphone interface element 185—or just by speakingwithout necessarily selecting element 185 (i.e., the automated assistantmay monitor for voice input during the providing)).

It is noted that textual input 480A is the same as textual input 380A,search result 482A is the same as search result 382A, textual input 488Bis the same as textual input 380B, and search result 482B is the same assearch result 382B. However, in FIG. 3 the textual input 380B is addedto the transcript of the dialog rendered in the graphical interface andpersists in the transcript. In contrast, the textual input 488B of FIG.4B is part of the dialog, but is not added to the transcript of thedialog rendered by the graphical interface. Also, in FIG. 3 the searchresult 382A persists in the transcript after search result 382B isprovided, whereas in FIG. 4B the search result 482B supplants the searchresult 482A thereby removing it from the transcript. In other words, thesearch result 482B replaces the search result 482A thereby preventingsimultaneous display of both search result 482A and search result 482B.In some implementations, technique(s) described with respect to FIGS.4A-4E may be beneficial, for example, for display screens of a limitedsize and/or for interfaces where it may be distracting and/orcomputationally burdensome to maintain a full transcript of the dialog.

After the search result 482B is displayed to the user, the user thenprovides textual input 488C of “next” as illustrated in FIG. 4C. Inresponse, the automated assistant 120 provides output that causes thesearch result 482B to be supplanted with the search result 482C asillustrated in FIG. 4C. The search result 482C is the next search resultaccording to the order.

After the search result 482C is displayed to the user, the user thenprovides textual input 488D of “back to the one from source 1”. Theautomated assistant 120 may determine that “back” references apreviously presented search result and that “source 1” is an attributeof the previously presented search result 482A. In response, theautomated assistant 120 causes search result 482C to be supplanted withexpanded search result 482D. Expanded search result 482D includes thesame content as search result 482A, but also includes some additionaltext from the underlying content item. From FIG. 4D, the user mayoptionally further navigate the search results (e.g., “Ok, now to searchresult 5”) or may select (e.g., an audible selection or a “touchselection”) the link associated with expanded search result 482D to viewthe full underlying content item associated with the expanded searchresult 482D. Viewing of the full underlying content item may optionallyoccur in a separate interface and/or a separate application.

FIG. 4E illustrates an example that is similar to that of FIGS. 4A-4D,but where various dialog items persist in the dialog. In particular, inFIG. 4E textual inputs 480B, 480C, and 480D correspond to textual inputs488B, 488C, and 488D of FIGS. 4B-4D. However, in FIG. 4E the textualinputs 480B, 480C, and 480D persist in the transcript of the dialog,whereas their counterparts do not in FIGS. 4B-4D. Also, in FIG. 4Esearch results 482A, 482B, and 482C persist in the transcript of thedialog, whereas they do not in FIGS. 4B-4D.

In FIG. 5 , the user provides initial textual input 580A as part of adialog between the user and the automated assistant 120. In response tothe textual input 580A, the automated assistant 120 obtains searchresults that are responsive to the textual input 580A and provides asearch result 582A for audible presentation via a speaker of thecomputing device 510. The automated assistant 120 provides the searchresult 582A first based on it being first in an order of presentation ofthe search results.

During the audible providing of the search result 582A, the userprovides textual input 582B that cuts off the providing of the searchresult 582A. The textual input 582B of “I never want news from Source 1”may be used to prevent further search results from Source 1 from beingprovided to the user by the automated assistant 120. For example, theautomated assistant 120 may suppress search results having an attributeof Source 1 based on the sentiment indicated by “never”. In someimplementations, further search results from Source 1 may be suppressedonly for those search results that are responsive to textual input 580A.In some other implementations, further search results from Source 1 mayalso be suppressed for search results that are responsive to furthertextual input. For instance, search results from Source 1 may continueto be suppressed by the automated assistant 120 until the userexplicitly indicates, via further textual input and/or other interfaceinput, that the user again wishes to receive search results fromSource 1. In some implementations, lesser magnitudes of negativesentiment may result in lesser durations and/or extents of suppressionof search results from Source 1. For example, “I'm not a big fan ofSource 1” may cause search results from Source 1 to be demoted, but notexcluded, based on sentiment associated with “not a big fan of” being ofa lesser magnitude than the sentiment associated with “never”. In someimplementations, the direction (i.e., positive, negative) and/ormagnitude of sentiment of terms associated with an attribute of a searchresult may be determined based on a sentiment classifier, one or morerules, and/or based on other techniques.

In response to the textual input 580B, the providing of search result582A is ceased and search result 582B is provided. In someimplementations, search result 582B is next in the order followingsearch result 582B. In some other implementations, one or moreintervening search results may be provided in the order between searchresult 582A and search result 582B, but the search result(s) skippedover by the automated assistant 120 based on them being associated witha source attribute of “Source 1.”

During the audible providing of the search result 582B, the userprovides textual input 580C that cuts off the providing of the searchresult 582B. In response to the textual input 580C of “next”, theautomated assistant may provide the next in the order search result582C. During the audible providing of the search result 582C, the userprovides textual input 580D that cuts off the providing of the searchresult 582C. In response to the textual input 580D of “next”, theautomated assistant may provide the next in the order search result582D.

During the audible providing of the search result 582D, the userprovides textual input 580E that cuts off the providing of the searchresult 582D. The automated assistant 120 may determine that “back” ofthe textual input 580E references a previously presented search resultand that “Local Business” is an attribute of the previously presentedsearch result 582B. In response, the automated assistant 120 againpresents search result 582B, with more of the search result 582B beingaudibly presented due to the user not cutting of the audiblepresentation via further textual input.

FIGS. 6 and 7 illustrate examples of how users may explore additionaldetail associated with a given search result via further dialog with theautomated assistant 120, and then navigate forwards/backwards to othersearch results.

In FIG. 6 , the user provides initial textual input 680A as part of adialog between the user and the automated assistant 120. In response tothe textual input 680A, the automated assistant 120 obtains searchresults that are responsive to the textual input 680A and provides asearch result 682A for presentation as part of a transcript of thedialog. The automated assistant 120 provides the search result 682Afirst based on it being first in an order of presentation of the searchresults.

The user then provides textual input 680B of “What time does it open?”to cause the automated assistant 120 to present additional detail 682Bthat is related to the search result 682A and that is responsive to thetextual input 680B. The user then provides textual input 680C of “next”.The automated assistant 120 may determine that “next” references thenext search result in the order and provide the next search result 682Cin response to the textual input 680C. In some implementations, theautomated assistant 120 may determine that “next” references the nextsearch result and not further detail about the search result 682A basedon the additional detail 682B being a singular item, instead of a listof items. In other words, there is only one “time that it opens” and“next” could not refer to an additional time that it opens.

In FIG. 7 , the textual input 780A is the same as the textual input 680Aof FIG. 6 and the search result 782A is the same as the search result682A of FIG. 6 . However, in FIG. 7 the textual input 780B differs fromthe textual input 680B of FIG. 6 . The textual input 780B of “Reviews?”causes the automated assistant 120 to present additional detail 782Bthat is related to the search result 782A and that is responsive to thetextual input 780B. The additional detail 782B is one of multipleavailable reviews for “Restaurant A.”

The user then provides textual input 780C of “next review”. Based on thepresence of “review”, the automated assistant 120 may determine that“next” references the next review, and not the next search result in theorder. Accordingly, in response to the textual input 780C, the automatedassistant 120 provides additional detail 782C that is another review of“Restaurant A”.

The user then provides textual input 780D of “next restaurant”. Based onthe presence of “restaurant”, the automated assistant 120 may determinethat “next” references the next restaurant search result. Accordingly,in response to the textual input 780D, the automated assistant 120provides additional search result 782D that is another restaurant searchresult responsive to textual input 780A.

FIG. 8 is a flowchart illustrating an example method 800 according toimplementations disclosed herein. For convenience, the operations of theflow chart are described with reference to a system that performs theoperations. This system may include various components of variouscomputer systems, such as automated assistant 120. Moreover, whileoperations of method 800 are shown in a particular order, this is notmeant to be limiting. One or more operations may be reordered, omittedor added.

At block 850, the system receives textual input. The textual input maybe generated as part of a dialog by a user and the system, and may bebased on user interface input generated by a user interface inputdevice, such as a microphone or virtual keyboard.

At block 852, the system obtains search results that are responsive tothe textual input and obtains a presentation order for the searchresults. The presentation order may be based on a ranking of the searchresults. In some implementations, the system itself performs a search toobtain the search results and/or rank the search results. In someimplementations, the system provides search parameters to one or moreseparate systems and obtains search results and a ranking of the searchresults in response.

At block 854, the system provides an initial group of the searchresults. For example, the system may provide only the first searchresult according to the presentation order. The initial group of thesearch results is provided for audible and/or visual presentation to theuser as part of the dialog.

At block 856, the system receives further textual input. The system thendetermines what type of input the further textual input is at one ormore of blocks 856A, 856B, 856C, and 856D.

If it is determined at block 856A that the further textual input iscontinuing input, the system proceeds to block 858. At block 858, thesystem provides the next group of search results according to thepresentation order. As one example of block 856A, the system maydetermine that the further textual input is continuing input if itincludes only one or more of a set of predefined “continuing” terms suchas “next”, “continue”, “go on”, etc. As one example of block 858, thesystem may provide only the next search result according to thepresentation order.

If instead it is determined at block 856B that the further textual inputis adaptation input, the system proceeds to block 860. At block 860, thesystem modifies the presentation order based on the further textualinput. The system then proceeds to block 858 and provides the next groupof search results according to the presentation order (as modified atblock 860). The system may additionally or alternatively proceed toblock 862, where the system determines and stores parameter(s) based onthe further textual input. As described herein, the parameters maymodify identification and/or ranking of search results in downstreamiterations of block 852.

As one example of block 856B, the system may determine that the furthertextual input is adaptation input if it includes one or more of a set ofpredefined “adaptation” terms (e.g., “back to”, “forward to”, “morelike”, “less like”, “less about”, “never”, “remove”) along with otherterm(s), conforms to one or more adaptation templates (e.g., “back toresult [#]”, “forward to result [#]”), and/or based on other techniques.As one example of block 860, the system may modify the presentationorder to promote, demote, or remove one or more search results having anattribute explicitly or implicitly referenced in the further textualinput.

If instead it is determined at block 856C that the further textual inputis result detail input, the system proceeds to block 864. At block 864,the system provides further search result detail based on the input. Asone example of block 856C, the system may determine that the furthertextual input is result detail input if it requests further contentrelated to search result(s) provided at block 854. For instance, if a“restaurant” search result is provided at block 854, further textualinput of “reviews for this restaurant” may be determined to be resultdetail input.

If instead it is determined at block 856D that the further textual inputis other input, the system proceeds to block 866 and performs otheraction(s). For example, the textual input may be input intended toinitiate a new search and the other actions of block 866 may be toproceed back to block 852 and obtain search results that are responsiveto the further textual input. Also, for example, the textual input maybe input that solicits a single response such as “what time is it” andthe further actions of block 866 may be to provide output that indicatesthe current time, then to return to block 856.

Multiple iterations of blocks 856, 856A/B/C/D, 858, 860, 862, 864,and/or 866 may occur during a dialog with a user to enable the user tonavigate and explore multiple search results according to techniquesdisclosed herein. Although blocks 856A, 856B, 856C, and 856D areillustrated in a particular order, it is understood the order may bealtered, one or more blocks may be performed in parallel, and/or one ormore blocks may only be selectively performed.

FIG. 9 is a flowchart illustrating an example method 900 according toimplementations disclosed herein. For convenience, the operations of theflow chart are described with reference to a system that performs theoperations. This system may include various components of variouscomputer systems, such as automated assistant 120. Moreover, whileoperations of method 900 are shown in a particular order, this is notmeant to be limiting. One or more operations may be reordered, omittedor added.

At block 950, the system receives textual input. The textual input maybe generated as part of a dialog by a user and the system, and may bebased on user interface input generated by a user interface inputdevice, such as a microphone or virtual keyboard.

At block 952, the system obtains search results that are responsive tothe textual input. In some implementations, at block 952 the system alsoobtains a presentation order for the search results.

At block 954, the system provides one or more groups of the searchresults. For example, the system may provide a first search result, thena second search result, then a third search result, etc.

At block 956, the system receives textual input during the providing ofblock 954.

At block 958, the system determines, based on the textual input, one ormore attributes of one or more search results provided at block 954. Forexample, the textual input may be “never show results from this source”.Based on “this source”, the system may determine a source attribute thatis assigned to the most recently provided search result. As anotherexample, the textual input may be “more like this”. Based on “likethis”, the system may determine an attribute that is assigned to themost recently provided search result such as a source attribute, a topicattribute, a document type attribute (e.g., image, video, or webpage),etc. As yet another example, the textual input may be “no more fromSource 1” and the system may determine a source attribute of “Source 1”that is also a source attribute of one or more of the search resultsprovided at block 954.

At block 960, the system determines a parameter based on the determinedattribute(s) and sentiment expressed for that attribute in the furthertextual input of block 956. A given parameter may define an attributeand how to influence search results having that attribute. The systemmay determine how to influence search results having that attributebased on the sentiment expressed for that attribute in the furthertextual input. In some implementations, the system may access a mappingof certain sentiment terms to certain influences to determine theinfluence. For instance, “never” may be mapped to suppressing searchresults having that attribute, “don't like” may be mapped to demoting toa first degree search results having that attribute, “really don't like”may be mapped to demoting to a greater second degree search resultshaving that attribute, “more like” may be mapped to promoting to a firstdegree search results having that attribute, etc. In someimplementations, the system may additionally or alternatively utilize asentiment classifier to determine a direction and/or magnitude ofsentiment, and may determine how to influence search results having thatattribute based on the direction and/or magnitude

At block 962, the system uses the parameter to influence whether or whenone or more attribute search results having that attribute are providedfor presentation to the user. In some implementations, the system usesthe parameter to influence one or more of the search results obtained atblock 952 and resultantly influence further providing of search resultsin response to the textual input received at block 950. In someimplementations, the system additionally or alternatively uses theparameter to influence one or more search results for downstreamsearches based on downstream textual input in dialog between the userand the system.

Various examples described herein are described with respect to visualpresentation of search results via a graphical user interface and/oraudible presentation of search results via a speaker. However, in someimplementations search results may be provided for presentation to theuser using additional and/or alternative techniques. For example, insome implementations search results may be provided for tactilepresentation to a user. As another example, in some implementationssearch results may include a plurality of lighting commands and/orlighting scenes to be implemented by a lighting system and each of thesearch results may be presented as lighting output from the lightingsystem. For instance, providing a lighting scene search result forpresentation to a user may include providing command(s) or other outputto a lighting system controller and/or to individual components of thelighting system (e.g., individual bulbs or other lighting units) thatcause a lighting scene that corresponds to the lighting scene searchresult to be generated by the lighting system. As one particularexample, a first lighting scene search result may be presented to theuser as lighting output from the lighting system; in response to “next”user interface input, the next lighting scene in an order of the searchresults may be presented; in response to another “next” user interfaceinput, the next lighting scene in the order may be presented; and inresponse to further user interface input that identifies a previouslypresented search result, another search result may be presented that iscounter to the order of the search results. For example, further textualinput of “back to the first lighting scene” may cause the initiallypresented lighting scene to again be presented, further textual input of“back to the one with a lot of red” may cause a previously providedlighting scene with an attribute of “red” to again be presented, furthertextual input of “more like this” may cause a yet to be presented sceneto be presented, where that scene is counter to the order and isidentified based on sharing attribute(s) with the currently presentedscene. As another particular example, a given lighting scene searchresult may be presented to the user as lighting output from the lightingsystem and, while the given lighting scene search result is beingpresented, the user may provide further user interface input of “neverany lighting scenes like this”. Such further user interface input may beutilized to determine whether or when further lighting scene searchresults having one or more attributes in common with the given lightingscene search result are presented in response to further dialog.

FIG. 10 is a block diagram of an example computing device 1010 that mayoptionally be utilized to perform one or more aspects of techniquesdescribed herein. In some implementations, one or more of a clientcomputing device, automated assistant 120, and/or other component(s) maycomprise one or more components of the example computing device 1010.

Computing device 1010 typically includes at least one processor 1014which communicates with a number of peripheral devices via bus subsystem1012. These peripheral devices may include a storage subsystem 1024,including, for example, a memory subsystem 1025 and a file storagesubsystem 1026, user interface output devices 1020, user interface inputdevices 1022, and a network interface subsystem 1016. The input andoutput devices allow user interaction with computing device 1010.Network interface subsystem 1016 provides an interface to outsidenetworks and is coupled to corresponding interface devices in othercomputing devices.

User interface input devices 1022 may include a keyboard, pointingdevices such as a mouse, trackball, touchpad, or graphics tablet, ascanner, a touchscreen incorporated into the display, audio inputdevices such as voice recognition systems, microphones, and/or othertypes of input devices. In general, use of the term “input device” isintended to include all possible types of devices and ways to inputinformation into computing device 1010 or onto a communication network.

User interface output devices 1020 may include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices. The display subsystem may include a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), a projectiondevice, or some other mechanism for creating a visible image. Thedisplay subsystem may also provide non-visual display such as via audiooutput devices. In general, use of the term “output device” is intendedto include all possible types of devices and ways to output informationfrom computing device 1010 to the user or to another machine orcomputing device.

Storage subsystem 1024 stores programming and data constructs thatprovide the functionality of some or all of the modules describedherein. For example, the storage subsystem 1024 may include the logic toperform selected aspects of the method of FIG. 8 and/or the method ofFIG. 9 .

These software modules are generally executed by processor 1014 alone orin combination with other processors. Memory 1025 used in the storagesubsystem 1024 can include a number of memories including a main randomaccess memory (RAM) 630 for storage of instructions and data duringprogram execution and a read only memory (ROM) 632 in which fixedinstructions are stored. A file storage subsystem 1026 can providepersistent storage for program and data files, and may include a harddisk drive, a floppy disk drive along with associated removable media, aCD-ROM drive, an optical drive, or removable media cartridges. Themodules implementing the functionality of certain implementations may bestored by file storage subsystem 1026 in the storage subsystem 1024, orin other machines accessible by the processor(s) 1014.

Bus subsystem 1012 provides a mechanism for letting the variouscomponents and subsystems of computing device 1010 communicate with eachother as intended. Although bus subsystem 1012 is shown schematically asa single bus, alternative implementations of the bus subsystem may usemultiple busses.

Computing device 1010 can be of varying types including a workstation,server, computing cluster, blade server, server farm, or any other dataprocessing system or computing device. Due to the ever-changing natureof computers and networks, the description of computing device 1010depicted in FIG. 10 is intended only as a specific example for purposesof illustrating some implementations. Many other configurations ofcomputing device 1010 are possible having more or fewer components thanthe computing device depicted in FIG. 10 .

In situations in which the systems described herein collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, social actions or activities, profession, a user'spreferences, or a user's current geographic location), or to controlwhether and/or how to receive content from the content server that maybe more relevant to the user. Also, certain data may be treated in oneor more ways before it is stored or used, so that personal identifiableinformation is removed. For example, a user's identity may be treated sothat no personal identifiable information can be determined for theuser, or a user's geographic location may be generalized wheregeographic location information is obtained (such as to a city, ZIPcode, or state level), so that a particular geographic location of auser cannot be determined. Thus, the user may have control over howinformation is collected about the user and/or used.

While several implementations have been described and illustratedherein, a variety of other means and/or structures for performing thefunction and/or obtaining the results and/or one or more of theadvantages described herein may be utilized, and each of such variationsand/or modifications is deemed to be within the scope of theimplementations described herein. More generally, all parameters,dimensions, materials, and configurations described herein are meant tobe exemplary and that the actual parameters, dimensions, materials,and/or configurations will depend upon the specific application orapplications for which the teachings is/are used. Those skilled in theart will recognize, or be able to ascertain using no more than routineexperimentation, many equivalents to the specific implementationsdescribed herein. It is, therefore, to be understood that the foregoingimplementations are presented by way of example only and that, withinthe scope of the appended claims and equivalents thereto,implementations may be practiced otherwise than as specificallydescribed and claimed. Implementations of the present disclosure aredirected to each individual feature, system, article, material, kit,and/or method described herein. In addition, any combination of two ormore such features, systems, articles, materials, kits, and/or methods,if such features, systems, articles, materials, kits, and/or methods arenot mutually inconsistent, is included within the scope of the presentdisclosure.

What is claimed is:
 1. A method implemented by one or more processors,comprising: receiving a spoken input of a user, the spoken input beingdetected, via at least one microphone of a client device, as part ofdialog between the user and an automated assistant implemented at leastin part by the client device; obtaining a plurality of groups oflighting commands that are responsive to the spoken input, wherein eachof the groups of lighting commands, when implemented, cause one or morecorresponding lighting components of a lighting system of the user to becontrolled in a manner that is different relative to other of the groupsof lighting commands; sequentially causing the groups of lightingcommands to be implemented by the lighting system of the user to controlone or more of the corresponding lighting components; receiving anadditional spoken input of the user, the spoken input being detected,via the at least one microphone of the client device, as part of thedialog between the user and the automated assistant during thesequentially implementing of the groups of lighting commands;determining that at least one term of the additional spoken inputmatches an attribute of a previously implemented group of lightingcommands, wherein the attribute is a reference to an implementationorder of the previously implemented group of lighting commands in thesequentially implementing or is a reference to a lighting scene of thepreviously implemented group of lighting commands in the sequentiallyimplementing; and in response to determining that the at least one termof the additional spoken input matches the attribute of the previouslyimplemented group of lighting commands: causing one or more of thecorresponding lighting components of the lighting system to becontrolled based on the previously implemented group of lightingcommands.
 2. The method of claim 1, wherein the reference to thelighting scene of the previously implemented group of lighting commandsin the sequentially implementing includes a reference to a color of oneor more of the corresponding lighting components of the lighting system.3. The method of claim 2, wherein causing one or more of thecorresponding lighting components of the lighting system to becontrolled based on the previously implemented group of lightingcommands is counter to an order of the groups of lighting commands beingsequentially implemented.
 4. The method of claim 1, further comprising:receiving a further additional spoken input of the user, the furtherspoken input being detected, via the at least one microphone of theclient device, as part of the dialog between the user and the automatedassistant during the sequentially implementing of the groups of lightingcommands; determining, based on processing the further additional spokeninput of the user, that the further additional spoken input references:the attribute of a given implemented group of lighting commands, and asentiment expressed by the user towards the attribute; and in responseto determining that the further additional spoken input references theattribute of the given implemented group of lighting commands and thesentiment expressed by the user towards the attribute: determining,based on the attribute and the sentiment expressed by the user towardsthe attribute, whether or when to provide, as part of the dialog, anadditional group of lighting commands that also has the attribute. 5.The method of claim 4, further comprising: as part of subsequent dialogbetween the user and the automated assistant implemented at least inpart by the client device that is subsequent to the dialog: determining,based on the attribute and the sentiment expressed by the user towardsthe attribute, whether or when to provide, as part of the subsequentdialog, an additional group of lighting commands that also has theattribute.
 6. The method of claim 1, wherein sequentially causing eachof the groups of lighting commands to be implemented by the lightingsystem of the user comprises: receiving a further additional spokeninput of the user, the further spoken input being detected, via the atleast one microphone of the client device, as part of the dialog betweenthe user and the automated assistant during the sequentiallyimplementing of the groups of lighting commands; and determining, basedon processing the further additional spoken input of the user, toimplement a next group of lighting commands.
 7. At least onenon-transitory computer-readable medium storing instructions that, inresponse to execution by one or more processors, cause the one or moreprocessors to perform the following operations: receiving a spoken inputof a user, the spoken input being detected, via at least one microphoneof a client device, as part of dialog between the user and an automatedassistant implemented at least in part by the client device; obtaining aplurality of groups of lighting commands that are responsive to thespoken input, wherein each of the groups of lighting commands, whenimplemented, cause one or more corresponding lighting components of alighting system of the user to be controlled in a manner that isdifferent relative to other of the groups of lighting commands;sequentially causing the groups of lighting commands to be implementedby the lighting system of the user to control one or more of thecorresponding lighting components; receiving an additional spoken inputof the user, the spoken input being detected, via the at least onemicrophone of the client device, as part of the dialog between the userand the automated assistant during the sequentially implementing of thegroups of lighting commands; determining that at least one term of theadditional spoken input matches an attribute of a previously implementedgroup of lighting commands, wherein the attribute is a reference to animplementation order of the previously implemented group of lightingcommands in the sequentially implementing or is a reference to alighting scene of the previously implemented group of lighting commandsin the sequentially implementing; and in response to determining thatthe at least one term of the additional spoken input matches theattribute of the previously implemented group of lighting commands:causing one or more of the corresponding lighting components of thelighting system to be controlled based on the previously implementedgroup of lighting commands.
 8. The at least one non-transitorycomputer-readable medium of claim 7, wherein the reference to thelighting scene of the previously implemented group of lighting commandsin the sequentially implementing includes a reference to a color of oneor more of the corresponding lighting components of the lighting system.9. The at least one non-transitory computer-readable medium of claim 8,wherein causing one or more of the corresponding lighting components ofthe lighting system to be controlled based on the previously implementedgroup of lighting commands is counter to an order of the groups oflighting commands being sequentially implemented.
 10. The at least onenon-transitory computer-readable medium of claim 7, wherein theinstructions further cause the one or more processors to perform thefollowing operations: receiving a further additional spoken input of theuser, the further spoken input being detected, via the at least onemicrophone of the client device, as part of the dialog between the userand the automated assistant during the sequentially implementing of thegroups of lighting commands; determining, based on processing thefurther additional spoken input of the user, that the further additionalspoken input references: the attribute of a given implemented group oflighting commands, and a sentiment expressed by the user towards theattribute; and in response to determining that the further additionalspoken input references the attribute of the given implemented group oflighting commands and the sentiment expressed by the user towards theattribute: determining, based on the attribute and the sentimentexpressed by the user towards the attribute, whether or when to provide,as part of the dialog, an additional group of lighting commands thatalso has the attribute.
 11. The at least one non-transitorycomputer-readable medium of claim 10, wherein the instructions furthercause the one or more processors to perform the following operations: aspart of subsequent dialog between the user and the automated assistantimplemented at least in part by the client device that is subsequent tothe dialog: determining, based on the attribute and the sentimentexpressed by the user towards the attribute, whether or when to provide,as part of the subsequent dialog, an additional group of lightingcommands that also has the attribute.
 12. The at least onenon-transitory computer-readable medium of claim 7, wherein theinstructions for sequentially causing each of the groups of lightingcommands to be implemented by the lighting system of the user furthercause the one or more processors to perform the following operations:receiving a further additional spoken input of the user, the furtherspoken input being detected, via the at least one microphone of theclient device, as part of the dialog between the user and the automatedassistant during the sequentially implementing of the groups of lightingcommands; and determining, based on processing the further additionalspoken input of the user, to implement a next group of lightingcommands.
 13. A system, comprising: at least one processor; and memorystoring instructions that, when executed by the at least one processor,cause the at least one processor to: receive a spoken input of a user,the spoken input being detected, via at least one microphone of a clientdevice, as part of dialog between the user and an automated assistantimplemented at least in part by the client device; obtain a plurality ofgroups of lighting commands that are responsive to the spoken input,wherein each of the groups of lighting commands, when implemented, causeone or more corresponding lighting components of a lighting system ofthe user to be controlled in a manner that is different relative toother of the groups of lighting commands; sequentially cause the groupsof lighting commands to be implemented by the lighting system of theuser to control one or more of the corresponding lighting components;receive an additional spoken input of the user, the spoken input beingdetected, via the at least one microphone of the client device, as partof the dialog between the user and the automated assistant during thesequentially implementing of the groups of lighting commands; determinethat at least one term of the additional spoken input matches anattribute of a previously implemented group of lighting commands,wherein the attribute is a reference to an implementation order of thepreviously implemented group of lighting commands in the sequentiallyimplementing or is a reference to a lighting scene of the previouslyimplemented group of lighting commands in the sequentially implementing;and in response to determining that the at least one term of theadditional spoken input matches the attribute of the previouslyimplemented group of lighting commands: cause one or more of thecorresponding lighting components of the lighting system to becontrolled based on the previously implemented group of lightingcommands.
 14. The system of claim 13, wherein the reference to thelighting scene of the previously implemented group of lighting commandsin the sequentially implementing includes a reference to a color of oneor more of the corresponding lighting components of the lighting system.15. The system of claim 14, wherein causing one or more of thecorresponding lighting components of the lighting system to becontrolled based on the previously implemented group of lightingcommands is counter to an order of the groups of lighting commands beingsequentially implemented.
 16. The system of claim 14, wherein theinstructions further cause the at least one processor to: receive afurther additional spoken input of the user, the further spoken inputbeing detected, via the at least one microphone of the client device, aspart of the dialog between the user and the automated assistant duringthe sequentially implementing of the groups of lighting commands;determine, based on processing the further additional spoken input ofthe user, that the further additional spoken input references: theattribute of a given implemented group of lighting commands, and asentiment expressed by the user towards the attribute; and in responseto determining that the further additional spoken input references theattribute of the given implemented group of lighting commands and thesentiment expressed by the user towards the attribute: determine, basedon the attribute and the sentiment expressed by the user towards theattribute, whether or when to provide, as part of the dialog, anadditional group of lighting commands that also has the attribute. 17.The system of claim 16, wherein the instructions further cause the atleast one processor to: as part of subsequent dialog between the userand the automated assistant implemented at least in part by the clientdevice that is subsequent to the dialog: determine, based on theattribute and the sentiment expressed by the user towards the attribute,whether or when to provide, as part of the subsequent dialog, anadditional group of lighting commands that also has the attribute. 18.The system of claim 14, wherein the instructions to sequentially causeeach of the groups of lighting commands to be implemented by thelighting system of the user comprise instructions to: receive a furtheradditional spoken input of the user, the further spoken input beingdetected, via the at least one microphone of the client device, as partof the dialog between the user and the automated assistant during thesequentially implementing of the groups of lighting commands; anddetermine, based on processing the further additional spoken input ofthe user, to implement a next group of lighting commands.